Sunbelt XXXI International Network for Social Network ... - INSNA

The Duality Of Homophily: Relating Attributes To NetworksMelamed, David; Schoon, Eric; Breiger, Ronald L.2‐Mode NetworksMethods, Homophily, Two‐mode Networks, Profile SimilaritySAT.AM1Based on new analytic strategies introduced in Ronald L. Breiger’s INSNA keynote address, we developed a network of actor attributes based on General SocialSurvey (GSS) data. Specifically we turn the conventional cases‐by‐variables matrix inside out, generating a cases‐by‐cases matrix that has heretofore receivedlittle attention. The diagonal of this matrix (i.e., the projection matrix in a regression context) is informative for regression diagnostics and for relating the casesto some outcome; however, we focus on the off‐diagonal cells in the matrix that relates the cases to each other. By clustering on the matrix of cases‐by‐caseswe identify groups of cases that are similar in their profiles across the attributes. We apply this innovative two‐mode analysis to ego network data from theGSS, predicting overall homophily and specific types of homophily‐‐ age, race and education‐‐ in respondents’ networks. Our results suggest that a modelincorporating clusters based on the “usual suspects” (e.g., race, sex, education, age, etc.) indeed predicts instances of homophily even after controlling for theusual suspects (treated as linear additive effects). We discuss which attributes yield a significant contribution to the formation of each identified cluster, andthe effects that the clusters have on the homophily outcomes. We conclude with how our research builds on and fits with the tradition of homophily analysisof social networks, and we discuss future directions of the profile similarity methodology.The Dynamics Of Degree Distributions In Flow Networks: Power Laws Without Rich‐get‐richer‐based AlgorithmsChu‐Shore, Jesse C.; Chu‐Shore, Catherine J.; Bianchi, Matt T.Network DynamicsNull Models, Power Law, Degree distributions, Flow Networks, Random Graph ModelsTHURS.PM2Power‐law degree distributions have been found to characterize a wide range of complex networks, and there is a growing literature on how this topologymight arise. However, this literature does not consider an important class of network: those in which links represent flow of some quantity from node to node.In these networks, throughput–and thus maximum outdegree–depends on how much flow it receives from “upstream” portions of the network. Likewise, linkdissolution and node death are likely to have cascading consequences for “downstream” nodes and links. Network structure and flow dynamics are thusinterdependent, but the literature does not address this issue. Here, we introduce a new category of random graph model for flow‐type networks, anddescribe the conditions under which a power law degree distribution is likely to emerge. Our contribution is novel in that the power law does not depend onnetwork growth or the rich‐get richer attachment rules which are the basis of other models in the literature.